Role Description This is a fraud domain role first. Data science is your toolkit, not your identity. - Own fraud detection strategy for our profiling engine. - Develop models and shape detection logic. - Work directly with clients to understand their fraud problems and translate them into solutions. - Engage with risk teams at financial services organizations. - Run iterative model development before handing production-ready specs to engineering. - Be the fraud expert in every room. Qualifications - Deep knowledge of fraud typologies: synthetic identities, first-party fraud, fraud rings, mule accounts. - Proven track record deploying fraud models into production environments. - Strong Python and ML framework experience. - Solid understanding of MLOps: deployment, monitoring, challenger models, performance decay. - Experience working with real-world fraud data and messy identity information. - Comfortable being the fraud authority in the room, without a playbook handed to you. Requirements - Built fraud detection models in production. - Knowledge of synthetic identity fraud in the data. - Experience managing the false positive problem and understanding the commercial pressure that comes with it. - Ready to own a function as the fraud expert that everyone else relies on. Benefits - Comprehensive health and wellness plans. - Paid time off and company holidays. - Flexible and remote-friendly opportunities. - Maternity/paternity leave. Company Description Provenir is a global fintech company with offices across North America, the UK, and Singapore backed by talented teams across APAC, EMEA, and LATAM. Provenir helps fintechs, financial institutions, and payment providers make smarter decisions, faster. We are passionate about technology and empowering businesses to become industry leaders.

• Play a strategic role in advancing the capabilities of Cotality’s flagship geocoding product. • Join a cross-functional team of product managers, geospatial data scientists, software engineers and architects engaged in the mission of providing best in class address standardization and geocoding services to our external and internal customers. • Build robust systems to handle the nuances of US addressing. • Innovate new mechanisms to transition geolocation precision from areal to specific. • Develop validation loops using multiple contributor data sources to verify geocoded points. • Evaluate and benchmark third-party geocoding services. • Evolve automated “confidence” scoring systems to measure and report on addressing and geocoding data over time. • Collaborate with the engineering team to take features from idea to production. • Work with in-house data acquisition team to evaluate data vendors.

• Support product, sales, leadership and marketing teams with data insights • Mine and analyze data from company databases to drive optimization and improvement • Assess the effectiveness and accuracy of new data sources and data gathering techniques • Develop custom data models and algorithms • Use predictive modeling to enhance customer experiences and revenue generation • Coordinate with functional teams to implement models and monitor outcomes • Develop processes to monitor model performance and data accuracy

Who is Cover Whale? Cover Whale is the nation’s leading commercial trucking insurtech. We improve road safety by combining the insurance products we sell with our telematics-based, data-driven driver coaching and safety program known as DriveSmart. Our program is proven to save lives while delivering better insurance for our drivers. For more information, please visit www.coverwhale.com.  Join us in the mission! The Role: We are seeking a highly analytical, technically strong Actuarial/Data Science professional to own and evolve our end-to-end reserving and predictive modeling capabilities. This role will maintain and enhance our Python-based reserving models while building predictive models using live telematics and claims data to drive pricing, cancellation strategy, and large loss detection. The position sits at the core of our underwriting and growth strategy, directly influencing dynamic pricing, loss ratio performance, and profitable expansion. This is a very exciting and high-impact job that involves a lot of thinking, research, and creativity. Responsibilities:  - Own and continuously improve the end-to-end reserving process, including maintaining and enhancing Python-based reserving models to improve accuracy and efficiency. - Develop and deploy predictive models using telematics, claims, and policy data to improve pre-bind and post-bind risk selection, with measurable impact on ULR and cancellation performance. - Build and refine large loss models using claims notes and historical data to improve early identification of high-risk claims. - Design and implement dynamic pricing frameworks and capabilities leveraging live market and portfolio data. - Translate complex data insights into clear actionable business recommendations for underwriting, growth, and leadership teams. - Produce carrier-level reporting deliverables and analytical outputs that meet high-quality and compliance standards. - Create agency-level and geographic performance insights to guide growth strategy and strategic initiatives. - Automate recurring analyses and reporting workflows to increase speed, scalability, and reliability.